Pytorch Implementations of Semi-Supervised Classification with Graph Convolutional Networks and Graph Attention Networks.
Overview of GAT.
<Cora dataset>
| Training Time | Loss | Acc | |
|---|---|---|---|
| GCN | 2s | 0.9790 | 81.6% |
| GAT | 17m | 0.6724 | 83.8% |
| spGCN | 13s | 0.9215 | 81.4% |
| spGAT | 1m30s | 0.6745 | 84.7% |
<Siteseer dataset>
| Training Time | Loss | Acc | |
|---|---|---|---|
| GCN | 2s | 1.2088 | 60% |
| GAT | 21m | 1.1907 | 59.1% |
| spGCN | 17s | 1.602 | 58.3% |
| spGAT | 1m47s | 1.1591 | 59.2% |
GAT achieves better performances compared to GCN.
python3 train.py --model {gcn, gat, spgcn, spgat}
https://github.com/tkipf/pygcn https://github.com/Diego999/pyGAT